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license: mit
task_categories:
- graph-ml
- feature-extraction
language:
- en
size_categories:
- 1M<n<10M
tags:
- knowledge-graph
- graph-neural-networks
- entity-linking
- relationship-extraction
pretty_name: DropThe Entity Relationship Graph
---
# DropThe Entity Relationship Graph
A large-scale entity relationship dataset containing **2.9 million typed, directional connections** between 1.8 million entities spanning entertainment, media, finance, and technology. Extracted from the [DropThe](https://dropthe.org) knowledge graph.
## Dataset Description
While most open knowledge graphs focus on encyclopedic facts (Wikidata) or narrow domains (MovieLens for ratings), this dataset captures **operational relationships** -- the connections that actually matter for building recommendation systems, search engines, and analytical tools. Every edge is typed, directional, and timestamped.
### Link Types
| Link Type | Count | Example |
|-----------|-------|---------|
| `acted_in` | 820,000+ | Florence Pugh -> Oppenheimer |
| `directed` | 96,000+ | Christopher Nolan -> Oppenheimer |
| `produced` | 145,000+ | Emma Thomas -> Oppenheimer |
| `works_at` | 230,000+ | Tim Cook -> Apple |
| `founded` | 18,000+ | Jensen Huang -> NVIDIA |
| `distributed_by` | 52,000+ | Oppenheimer -> Universal Pictures |
| `related_to` | 1,200,000+ | Bitcoin -> Ethereum (thematic) |
| `traded_on` | 340,000+ | Bitcoin -> Binance |
| `parent_of` | 46,000+ | Alphabet -> Google |
### Graph Statistics
- **Nodes**: 1,828,455 entities across 5 types (movies, series, people, companies, cryptocurrencies)
- **Edges**: 2,947,733 typed directional links
- **Average degree**: 3.2 connections per entity
- **Largest connected component**: Covers 94% of all entities
- **Diameter**: 8 hops (entertainment subgraph)
### Data Format
Each edge record contains:
- **source_id**: Entity ID of the origin node
- **target_id**: Entity ID of the destination node
- **link_type**: Typed relationship label (see table above)
- **weight**: Confidence score (0.0-1.0) based on source reliability
- **created_at**: Timestamp of link creation
- **source_table**: Whether the entity comes from `entities` or `geo_entities`
## Intended Use
This graph dataset is particularly suited for:
- **Graph neural networks** -- Train GCN, GAT, or GraphSAGE models for link prediction and node classification
- **Knowledge graph completion** -- Predict missing edges using TransE, RotatE, or other embedding methods
- **Recommendation systems** -- Build multi-hop recommendation paths (actor -> movie -> director -> other movies)
- **Community detection** -- Identify clusters in the entertainment industry (studios, talent agencies, franchise ecosystems)
- **Temporal analysis** -- Study how industry relationships evolve over time using edge timestamps
## Methodology
Links are extracted through a combination of structured API ingestion (TMDB, Wikidata, CoinGecko), automated entity resolution using the DropThe alias system (80,000+ aliases), and a bidirectional auto-linker that ensures graph consistency. The full pipeline is described on [DropThe](https://dropthe.org).
The enrichment process runs on a local PostgreSQL instance with validation gates that check referential integrity, duplicate detection, and type conformity before any edges are promoted to the production graph on [DropThe](https://dropthe.org/data/).
## Sample
The included `sample_edges.csv` contains 20 representative edges across multiple relationship types, demonstrating the schema and data format.
## License
MIT License.
## Citation
```
@dataset{dropthe_graph_2026,
title={DropThe Entity Relationship Graph},
author={DropThe},
year={2026},
url={https://dropthe.org}
}
```
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